- July 21, 2021
- Posted by: Admin
- Category: Technology
Artificial Intelligence holds the key place in the huge transformation of the Automotive Industry integrating them into designing autonomous cars. Along with different areas like supply chain management, manufacturing operations, mobility services, images, and video analytics, audio analytic plays a significant role in making self-driving cars.
The automotive industry is reshaping and adopting the latest technologies. Audio analytics for autonomous vehicle is one of those technologies which has enhanced control for the driver.
Audio analytics has become a reality
Self-driving cars are no longer confined to science fiction movies with the arrival of autonomous drive functionality. Technological breakthroughs like 5G connectivity and artificial intelligence are making it possible for cars to drive themselves.
Earlier, voice and speech recognition was a challenge without the help of fast processing power, efficient algorithms, and reliable connectivity. Moreover, the car cabin reduced the performance of the audio analytics which resulted in false recognition.
These analytics have always been a part of constant research for a long time. Audio analytics consists of various components like NLP, voice/speech, and sound recognition. The innovative audio analytics have started utilizing the uninterrupted wireless internet service and cloud computing technology.
Various Machine learning algorithms like k nearest neighbor, support vector theorem, ensemble bagged trees, deep neural networks, and natural language processing for autonomous vehicle is involved to make it more effective.
The audio data is pre-processed to remove the noise and then the audio feature can be extracted from the audio data in a hassle-free manner. MFCC(Mel-frequency cepstral coefficient), Kurtosis, Variance are used here. Data is pre-processed to remove the noise and technically audio features like MFCC(Mel- Frequency cepstral coefficient ) and statistical features like Kurtosis and Variance are utilized. After the frequency bands of MFCC are spaced on the Mel scale, the trained model is utilized for interference. A real-time audio stream is utilized from the multiple microphones installed in the car which can be processed and the features can be extracted. These extracted features can be passed to the trained model to recognize the audio to help you make the right decision in autonomous vehicles.
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Data Processing plays an important role
Customer’s trust is something which is the most important thing and natural language processing in autonomous vehicles allows passengers to gain some basic functions of the car like stop at a restaurant, select a different route, switch on/off lights, open and close the doors, and a lot more. These experiences enhance the interaction between the car and the human.
Let’s dig in some of the best experiences where audio analytics can be utilized:
1. Avoid any dangers
An autonomous car can easily avoid any dangerous situation, for instance, the machine learning models like support vector machines can be utilized to detect the sound of the siren of any emergency vehicle. A supervised learning model is utilized for both classification and regression analysis.
These classification models utilize the data of the emergency siren sound and non-emergency sounds. With the help of these detection systems, autonomous cars can allow people moving in the emergency vehicle to pass.
2. Honking plays an important role.
When you think of audio analytics for autonomous car, you understand it must function as a human for instance when there is any requirement to change the lane, it must indicate with honking. Random Forest is a great machine learning algorithm which works for supervised classification algorithm, it creates the forest of decision trees and adds decision trees to accurately classify. A system can identify the different types of horn which proves beneficial to take the decision accordingly.
The automatic early detection of engine failure can prove to be one of the beneficial features. Autonomous cars must detect any abnormalities since the car engine makes a different sound when it has some issues. These machine learning algorithms available among K-means clustering used to detect anomalies in engine sound.
Each data point is assigned to the k group of clusters in the terms of k-means clustering. Near the centroid of the cluster, data is assigned and in case of any anomalous sound, then the data point will fall outside the normal cluster. In this way, engine monitoring in autonomous cars can take place easily. An autonomous car can easily warn the user and make proper decisions to avoid dangerous situations.
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4. Sincerity at its best!
Natural Language Processing in autonomous vehicles helps to processes the human language to help extract the meaning to make decisions. It helps to speak to the autonomous car. It can understand your given commands and even the complex sentences too. For instance, when any person asks a car to take him to his favorite park. the car obeys it blind fondly. Since autonomous cars blindly follow the owner’s instructions, they must not end up in dangerous or life-threatening situations. These autonomous vehicles must require a more powerful NLP that interprets the human conversation and informs you of the consequences.
Machine learning algorithms allow self-driving cars to exist. In order to enhance driver convenience, these autonomous cars have started installing audio integration into cars. Using microphone’s embedded into the car, drivers give basic commands like asking for weather or news updates. They allow a car to collect data on its surroundings from cameras and other sensors, interpret it, and decide what actions to take. It even allows cars to behave like humans. These machine learning-based audio analytics help to the increasing popularity of autonomous vehicles. Machine Learning continues to leverage its service-based offerings such as audio analytics, NLP, voice recognition to enhance the on-road safety and maintenance of the car. At DxMinds, our artificial intelligence solution development team allows data for automotive AI solutions. If you want to create an autonomous AI system, you can send a message to our team.
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